Browsing by Author "Nyambo, Kudakwashe"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemMetallophiles as sources of antimycobacterial agents(Stellenbosch : Stellenbosch University, 2023-12) Nyambo, Kudakwashe; Mavumengwana, Vuyo; Ngxande, Mhkuseli; Smith, Liezel; Sithole-Niang, Idah; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences. Molecular Biology and Human Genetics.ENGLISH ABSTRACT: Mycobacterial pathogens present a significant complication to disease control globally due to their resistance to numerous antibiotics. The rise in resistant strains to current chemotherapeutic treatments has prompted the search, development and implementation of new strategies to address this challenge. Harnessing the bioactivity of natural products found in the vast chemical space by using multi-disciplinary approaches has emerged as a promising way to discover new Tuberculosis drugs. This study aimed to evaluate the potential antimycobacterial activity of secondary metabolites from bacteria, fungi, and plants in-vitro and in-silico. In addition to mining for Mycobacterium tuberculosis targets, this study went further to explore other druggable targets associated with cancer in order to fully explain exhaustive in-silico bioactivity profiles. The following experiments were conducted to satisfy the aims: (i) bacteria from gold mine tailings were isolated and identified using 16S rRNA sequencing. The crude extracts from the bacteria were screened for potential activity against Mycobacterium tuberculosis (M. tb) H37Rv, Mycobacterium smegmatis MC2155, and Mycobacterium aurum A+ in-vitro. The active extracts were tentatively identified using HPLC-qTOF, GNPS, and Ms Dial. The identified compounds were virtually screened against Mycobacterium Pks13 and PknG. The natural compound that displayed high affinity was subjected to modification through multiple synthetic routes using reaction-driven enumeration. (ii) A total of 15 fungi compounds from fungi isolated from gold mine tailings were evaluated for their potential activity against M. tb PknA, PknB, PknD, and PknE proteins using extra precision molecular docking, molecular dynamics simulations, and molecular mechanics generalized born surface area (MM-GBSA) binding free energy calculation. (iii) Genomic DNA of one bacterial colony that showed activity against M. tb, was isolated and sequenced by Illumina’s NextSeq platform. The genes responsible for producing metabolites that may have antimycobacterial activity were determined using antiSMASH and PARTIC. (iv) Predictive machine learning-based quantitative structure-activity relationship models were developed with a pIC50 as the dependable variable, while features extracted from compounds found to be active against InhA were the independent variable. Another approach in developing a multitargeted SMILES-based Long Short-term Memory (LSTM) based on pIC50, and small, skewed datasets was attempted. (v) Medicinal plant species indigenous to South Africa namely Schotia brachypetala, Rauvolfia caffra, Schinus molle, Ziziphus mucronate, and Senna petersiana were evaluated for their potential antimycobacterial activity against Mycobacterium smegmatis MC2155, Mycobacterium aurum A+, and M. tb H37Rv. Although the study was specific to mycobacteria, further exploration into cytotoxic activity against MDA-MB 231 triple-negative breast cancer cells was also attempted to see if druggable targets could also be identified in eukaryotic cells as a test of the utility and robustness of the method. The constituents of the extracts possessing antimycobacterial activity were virtually screened using a rigorous Virtual Screening Workflow. The compounds exhibiting good binding, and ADME properties were returned and subjected to molecular dynamics simulations. MM-GBSA calculations were performed to evaluate the affinity of the selected compound/s to pantothenate kinase (PanK). Crude extracts from three bacterial isolates, namely Bacillus subtilis and Bacillus licheniformis, exhibited activity against M. tb H37Rv, Mycobacterium smegmatis MC2155, and Mycobacterium aurum A+. The classes of secondary metabolites identified in this study are known to possess antibacterial activity. Virtual screening of the secondary metabolites against PknG and Pks13, returned cyclo-(L-Pro-4-OH-L-Leu) and vazabitide A with pre-MD MM-GBSA values of -42.81 kcal/mol and -47.62 kcal/mol, respectively. The modification of vazabitide A yielded a compound with a higher affinity of -85.80 kcal/mol to the Pks13, binding as revealed by the post-MD MM-GBSA. SAMN36381076 was assigned to be B. licheniformis whole genome analysis. The genome length of B. licheniformis SAMN36381076 was estimated to be 4.213156 Mb, with a G+C content of 46.08%, comprising 58 contigs and exhibiting an N50 length of 165,033 bp. The biosynthetic gene clusters identified included fengycin, butirosin A, butirosin B, schizokinen, pulcherriminic acid, bacillibactin, bacillibactin E, bacillibactin F, lichenicidin VK21 A1, Lichenicidin VK21 A2, and thermoactinoamide A. These gene clusters are known for producing secondary metabolites with antimicrobial activity. Furthermore, The B. licheniformis SAMN36381076 possesses genes that encode for six diverse antibiotic resistance mechanisms, with efflux pumps as the predominant mechanism of resistance. Metabolic analysis of B. licheniformis SAMN36381076 showed that the presence of genes involved carbohydrate degradation and assembly processes, oxyanion biogeochemical cycling, and nitrogen cycling. In-silico evaluation of fungi compounds against ser/thr kinases showed the lowest ΔGBind values of aurovertin D against PknA (-50.9 kcal/mol), aurovertin D against PknB (-50.7 kcal/mol), verticillin A against PknD (-36.8 kcal/mol), and roquefortine C against PknE (-53.4 kcal/mol). Molecular dynamics simulation showed that the PknD-verticillin A exhibited the highest stability. Furthermore, the post-MD MMGBSA ΔGBind showed that verticillin A has a high affinity for PknD -53.67 kcal/mol. The results indicated that verticillin A is a potential hit compound that can befurther optimized and modified to develop a potent antimycobacterial inhibitor. The classical machine learning models developed from logistic regression and multi-layer perceptron were identified to have significant performance metrics on the InhA dataset. The results (- R2) from the multitarget Long Short Term Memory (LSTM) model indicated the need for hyperparameter tuning. However, further external validation of the two classification models is needed. In this study, the bioactive compounds present in R. caffra and S. molle showed average activity against M. tb H37Rv (MIC 0.25-0.125 mg/mL). Norajmaline with a docking score of -7.47 kcal/mol, and pre-MM-GBSA of -37.64 kcal/mol was returned from the rigorous virtual screening. Molecular dynamics simulation and post-MD MM-GBSA revealed the stable binding of norajmaline to PanK (-58. 73 kcal/mol). Results from the Flow cytometry analysis of treated MDA-MB 231 cells revealed that the dichloromethane extracts from S. petersiana, Z. mucronate, and ethyl acetate extracts from R. caffra and S. molle induced higher levels of apoptosis than the control cisplatin. In conclusion, this study serves as a starting point for the in-silico discovery of potent antimycobacterial compounds from metallophiles (fungi and bacteria) and plants. Virtual screening accelerates the drug discovery process by identifying compounds that may possess activity, thus they can be modified to increase potency. The incorporation of a large dataset of compounds comprising different biological conditions but with the same endpoint can be used to develop robust models with exceptional generalization capabilities.